Beverly Hills, CA, US
Summary
Supported a research team in developing and testing machine learning models for natural language processing, contributing to data collection, model training, and performance analysis.
Highlights
Developed Python scripts to preprocess and clean large datasets (10,000+ entries) for NLP model training, improving data quality by 15%.
Assisted in the implementation and evaluation of various machine learning algorithms (e.g., SVM, Random Forest) for text classification, achieving an accuracy of 88%.
Collaborated with two senior researchers to analyze model outputs and identify areas for optimization, contributing to a 5% reduction in error rate.
Documented research methodologies and experimental results, ensuring clear communication and reproducibility of findings for future studies.